

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase			:				Any   =     {
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase			:				Optional[Any]   =     ["DistilBertTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase			:				List[str]   =     [
		    "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "DistilBertForMaskedLM",
		    "DistilBertForMultipleChoice",
		    "DistilBertForQuestionAnswering",
		    "DistilBertForSequenceClassification",
		    "DistilBertForTokenClassification",
		    "DistilBertModel",
		    "DistilBertPreTrainedModel",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase			:				List[Any]   =     [
		    "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFDistilBertForMaskedLM",
		    "TFDistilBertForMultipleChoice",
		    "TFDistilBertForQuestionAnswering",
		    "TFDistilBertForSequenceClassification",
		    "TFDistilBertForTokenClassification",
		    "TFDistilBertMainLayer",
		    "TFDistilBertModel",
		    "TFDistilBertPreTrainedModel",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase			:				Union[str, Any]   =     [
		    "FlaxDistilBertForMaskedLM",
		    "FlaxDistilBertForMultipleChoice",
		    "FlaxDistilBertForQuestionAnswering",
		    "FlaxDistilBertForSequenceClassification",
		    "FlaxDistilBertForTokenClassification",
		    "FlaxDistilBertModel",
		    "FlaxDistilBertPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_distilbert import (
		    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DistilBertConfig,
		    DistilBertOnnxConfig,
		)
		from .tokenization_distilbert import DistilBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_distilbert_fast import DistilBertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_distilbert import (
				    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DistilBertForMaskedLM,
				    DistilBertForMultipleChoice,
				    DistilBertForQuestionAnswering,
				    DistilBertForSequenceClassification,
				    DistilBertForTokenClassification,
				    DistilBertModel,
				    DistilBertPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_distilbert import (
				    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDistilBertForMaskedLM,
				    TFDistilBertForMultipleChoice,
				    TFDistilBertForQuestionAnswering,
				    TFDistilBertForSequenceClassification,
				    TFDistilBertForTokenClassification,
				    TFDistilBertMainLayer,
				    TFDistilBertModel,
				    TFDistilBertPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_distilbert import (
				    FlaxDistilBertForMaskedLM,
				    FlaxDistilBertForMultipleChoice,
				    FlaxDistilBertForQuestionAnswering,
				    FlaxDistilBertForSequenceClassification,
				    FlaxDistilBertForTokenClassification,
				    FlaxDistilBertModel,
				    FlaxDistilBertPreTrainedModel,
				)

else:
		import sys

		lowercase			:				List[Any]   =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase						)		->       Any:
														super().__init__()
														self.register_modules(vqvae=__UpperCAmelCase    ,						unet=__UpperCAmelCase    ,						scheduler=__UpperCAmelCase						)






							@torch.no_grad()
							def __call__(					self    ,						__UpperCAmelCase = 1    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = 0.0    ,						__UpperCAmelCase = 50    ,						__UpperCAmelCase = "pil"    ,						__UpperCAmelCase = True    ,						**__UpperCAmelCase    ,						)		->       Union[Tuple, ImagePipelineOutput]:
														A      :					List[Any]      		=					randn_tensor(
														    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    ,						generator=__UpperCAmelCase    ,						)
														A      :					List[Any]      		=					latents.to(self.device						)

														# scale the initial noise by the standard deviation required by the scheduler
														A      :					Optional[int]      		=					latents * self.scheduler.init_noise_sigma

														self.scheduler.set_timesteps(__UpperCAmelCase						)

														# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
														A      :					str      		=					'''eta''' in set(inspect.signature(self.scheduler.step						).parameters.keys()						)

														A      :					List[str]      		=					{}
														if accepts_eta:
																					A      :					Optional[Any]      		=					eta

														for t in self.progress_bar(self.scheduler.timesteps						):
																					A      :					Any      		=					self.scheduler.scale_model_input(__UpperCAmelCase    ,						__UpperCAmelCase						)
																					# predict the noise residual
																					A      :					Dict      		=					self.unet(__UpperCAmelCase    ,						__UpperCAmelCase						).sample
																					# compute the previous noisy sample x_t -> x_t-1
																					A      :					int      		=					self.scheduler.step(__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						**__UpperCAmelCase						).prev_sample

														# decode the image latents with the VAE
														A      :					str      		=					self.vqvae.decode(__UpperCAmelCase						).sample

														A      :					Tuple      		=					(image / 2 + 0.5).clamp(0    ,						1						)
														A      :					int      		=					image.cpu().permute(0    ,						2    ,						3    ,						1						).numpy()
														if output_type == "pil":
																					A      :					Union[str, Any]      		=					self.numpy_to_pil(__UpperCAmelCase						)

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=__UpperCAmelCase						)






